Moloco Inc.

Moloco Inc.

Senior Applied Scientist - Moloco Ads

Seattle, Washington, United States · Senior

Sponsorship not specified$188k-$235kDetected 26 days ago
AlgorithmsMachine LearningData ScienceA/B TestingResearchLeadershipCommunicationCollaborationPublic Speaking

About the role

  • The impact you'll be contributing to Moloco:
  • The Research Science (RS) team focuses on the operation and optimization of our software systems working closely with infrastructure engineering teams, machine learning teams and data science teams.
  • Work with other applied scientists on projects to evaluate the health of both internal and external components to make sure the Moloco system is running safely and efficiently.

Responsibilities

  • Your work will contribute to driving performance improvements and cost reductions, debugging and investigating production issues, and stabilizing our core system as you develop a deep end-to-end understanding of the system.
  • We take intelligent risks and make disciplined trade-offs to maintain deep focus.
  • Collaborate closely with product, engineering, and other scientists to iterate on experiments, define roadmaps, and translate business requirements into scientifically sound solutions.
  • We collaborate proactively and inclusively, involving the right people at the right time and in the right way.
  • We strive to create a more equitable workplace.

Requirements

  • Advanced degree in Computer Science, Mathematics or related field, or industry experience in optimization of market prices or ML systems
  • At least 4 years of engineering or applied science experience
  • Proficient verbal and written English communication skills, with the ability to contribute to the creation of presentations and reports

Nice to have

  • Eligibility and amounts are determined by performance and the terms of the applicable plans.

Skills

  • Join us today and apply now!

Compensation

  • U.S.-based employees also receive up to 12 scheduled paid holidays per calendar year and one Thrive Day off per quarter.
  • Eligibility and amounts are determined by performance and the terms of the applicable plans.
  • The location for this role is listed above.
  • For base pay range purposes, location-based compensation is grouped into the following regions.
  • Your region is determined by your assigned work location.
  • Region A: Menlo Park Office, New York Office, SF Bay Area, New York Metro Area

Benefits

  • Implement, and evaluate new algorithms and features in collaboration with senior Applied Scientists, Software Engineers and Machine Learning Engineers.
  • We're one team working towards one mission and vision.

Company info

  • Moloco Company Blog
  • Moloco Leadership
  • Moloco Newsroom
  • We help our customers win by delivering durable value.
  • Complete task as part of larger projects to complete deep unbiased analyses.

Equal opportunity

  • Creating a diverse workforce and a culture of inclusion and belonging is core to our existence. To reach our goals, diversity of talent and thought is a critical component of how we operate as an organization. Our workforce is our superpower, and we know that fostering a culture of inclusion, authenticity, and belonging gives us the greatest opportunity to achieve our vision to become the scaling engine for the Internet economy.
  • Moloco is an equal opportunity employer. We highly value diversity in our current and future employees and do not discriminate (including in our hiring and promotion practices) on the basis of race, color, creed, religion, national origin, age, sex and gender, gender expression and identity, sexual orientation, marital status, ancestry, physical or mental disability, military or veteran status, or any other characteristic protected by law.
  • Creating a diverse workforce and a culture of inclusion and belonging is core to our existence.
  • To reach our goals, diversity of talent and thought is a critical component of how we operate as an organization.

This listing is sourced directly from Moloco Inc.'s careers page and normalized into a canonical job model.